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reinforcement learning in RoboschoolInvertedPendulum-v1 environment
RoboschoolInvertedPendulum-v1
The text was updated successfully, but these errors were encountered:
experiment specifications hyperparameter:
# input layer(observations) input_ = Input(shape=self.obs_dim) # hidden layer 1 h1_ = Dense(24,kernel_initializer=GlorotNormal())(input_) h1_b = BatchNormalization()(h1_) h1 = Activation('relu')(h1_b) # hidden_layer 2 h2_ = Dense(16,kernel_initializer=GlorotNormal())(h1) h2_b = BatchNormalization()(h2_) h2 = Activation('relu')(h2_b) # output layer(actions) output_ = Dense(self.act_dim,kernel_initializer=GlorotNormal())(h2) output_b = BatchNormalization()(output_) output = Activation('tanh')(output_b) scalar = self.act_range * np.ones(self.act_dim) out = Lambda(lambda i: i * scalar)(output)
# input layer(observations and actions) input_obs = Input(shape=self.obs_dim) input_act = Input(shape=(self.act_dim,)) inputs = [input_obs,input_act] concat = Concatenate(axis=-1)(inputs) # hidden layer 1 h1_ = Dense(24, kernel_initializer=GlorotNormal(), kernel_regularizer=l2(0.01))(concat) h1_b = BatchNormalization()(h1_) h1 = Activation('relu')(h1_b) # hidden_layer 2 h2_ = Dense(16, kernel_initializer=GlorotNormal(), kernel_regularizer=l2(0.01))(h1) h2_b = BatchNormalization()(h2_) h2 = Activation('relu')(h2_b) # output layer(actions) output_ = Dense(1, kernel_initializer=GlorotNormal(), kernel_regularizer=l2(0.01))(h2) output_b = BatchNormalization()(output_) output = Activation('linear')(output_b)
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training time : about 5 hours(2500 epi) on intel i7 cpu
CUN-bjy
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reinforcement learning
in
RoboschoolInvertedPendulum-v1
environmentThe text was updated successfully, but these errors were encountered: